Project Summary/Abstract It is well known that normal anatomy in a medical image can mask the presence of disease. However this process is not well understood. Part of the problem is that we lack knowledge of the relevant statistical descriptors that characterize perceptual effects of image statistics. While image acquisition noise is largely characterized by its second moments (power-spectrum or covariance matrix), background anatomy has a complex structure that requires higher-order statistics ? and an understanding of their perceptual relevance ?to characterize fully. This is an important limitation because reading ?through? this background is a critical component of many clinical tasks. In a statistical sense, reading through the background means exploiting redundancies in the presentation of normal anatomical structures for the purpose of isolating disease processes. The need for background characterization is well recognized in screening mammography, our focus, as screening mammography typically includes an assessment of the background via the BIRADS density score. However, this score has limited utility as a statistical descriptor. The basis for this project is to translate a successful approach from basic vision science to medical imaging, in order to identify the relevant high-order statistical properties of medical images and their perceptual impact. In this approach, a set of local image statistics (co-occurrence probabilities) are used to build an ?alphabet? for the statistical structure of synthetic visual textures and their local features (such as edges). Perceptual sensitivities to local features can be concisely characterized and modeled via this alphabet, and it has been shown that sensitivity to these elements is matched to their informativeness in natural scenes. This motivates our general approach, and many specifics of our research plan. Our plan is to develop algorithms in Aim 1 that selectively alter (either increase or decrease) the co-occurrence statistics of mammograms, while retaining their general background appearance. The sub-aims explore four strategies, building on a Fourier domain approach for which a proof-of-principle is in hand. Then, Aim 2 will use these images to assess perceptual sensitivity. Aim 2A will develop the psychophysical paradigm. Aims 2B-D will determine whether the principles identified in previous studies of synthetic visual textures (sign-invariance, approximate scale-invariance, and quadratic combination) extend to medical images, as this will enable a comprehensive yet concise description of perceptual sensitivity. We will pursue these aims using a database of full-field digital mammograms. The project is expected to yield a validated approach for modulating high-order statistical properties of mammograms and baseline data of perceptual sensitivity to these modulations. These findings will improve our understanding how normal anatomy impacts the statistical properties of screening mammograms, and give us valuable baseline data on how the statistics of normal anatomy affect perception.